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Foreword:

In the Internet era, a lot of data will be produced in the network every day. Through the analysis of the data, how to better interpret the meaning behind the data, we need to visualize the data.

In data visualization, Python also supports the third module.

Matplotlib module: the most widely used visualization library in Python

Seaborn Module: graphic Visualization based on matplotlib

Pycharts module: a class library for generating Echarts diagrams

Next, we will learn the graphic methods provided by the matplotlib module.

1. Overview of matplotlib module

The matplotlib module is open source by a third party, developed by the John Hunter team and sponsored by NumFOCUS.

The matplotlib module is a comprehensive library for Python to create static, dynamic and interactive visualization.

Features of matplotlib module

Easy to create charts such as publication quality maps, interactive data can be zoomed in and out

Customized charts have complete control over line styles, import and embed multiple file formats

High scalability and compatibility with third-party modules

The information manual of matplotlib module is rich in information and can be used quickly.

Matplotlib module acquisition

Matplotlib is the mainstream third-party visualization module of Python. We need to download it using pip.

Pip install matplotlib

Matplotlib module usage

Among the matplotlib modules, the pyplot class is the most commonly used.

Method 1:

From matplotlib import pyplot

Method 2:

Import matplotlib.pyplot as plt

Important note:

Official data of matplotlib module

View matplotlib internal code description

2. Matplotlib.pyplot related methods

The matplotlib.pyplot module is one of the most commonly used modules for drawing symbols.

Method use the title of pyplot.title (name) chart pyplot.xlabel (name) X-axis name of pyplot.ylabel (name) chart y-axis name pyplot.show () print out chart pyplot.plot (xvalue,yvalue) draw broken-line chart pyplot.bar (xvalue,yvalue) draw column chart pyplot.axis (data) easy way to get or set some axis properties pyplot.scatter (data) draw scatter chart pyplot.subplot (data) draw subgraph pyplot.grid (boolean) display mesh Defaults to Falsepyplot.text () for text processing pyplot.pie (data) pie chart pyplot.boxplot (data) box chart pyplot.hist (data) histogram 3. Matplotlib.pyplot chart display

Draw a line chart

Use the pyplot..plot () method

From matplotlib import pyplot# sets the font format of the chart pyplot.rcParams ["font.sans-serif"] = ['SimHei'] pyplot.rcParams ["axes.unicode_minus"] = Falsepyplot.plot ([1mai, 2je, 3jingle, 4jingle, 5jingle, 6], [45pime, 20pyrrine, 56pje, 35pct 69]) pyplot.title ("data analyze") pyplot.xlabel ("data") pyplot.ylabel ("sum") pyplot.show ()

Draw a bar chart

Use the pyplot..bar () method

Using the above data again, you can see the histogram

Pyplot.bar ([1, 2, 3, 4, 5, 6], [45, 20, 19, 56, 56, 35, 69])

Draw a pie chart

Use the pyplot.pie () method to draw a pie chart

Use the pyplot.axis method to set each partition interval at the same time

From matplotlib import pyplotlabels = ["windows", "MAC", "ios", "Android", "other"] sizes = [50 sizes,explode=explode,labels=labels,autopct='%1.1f%%',shadow=False,startangle=90 10, 5, 15] explode= [0. 1, 0. 1, 0, 0] pyplot.pie (sizes,explode=explode,labels=labels,autopct='%1.1f%%',shadow=False,startangle=90) pyplot.axis ("equal") pyplot.title ("data analyze") pyplot.show ()

Draw scatter plot

Draw a scatter plot using pyplot.scatter (xQuery y)

Import numpy as npfrom matplotlib import pyplotdata = {"a": np.arange (50), "c": np.random.randint, "d": np.random.randn (50)} data ['b'] = data ['a'] + 10*np.random.randn (50) data ['d'] = np.abs (data ['d']) * 100pyplot.scatter ("a", "b", cymbals Data=data) pyplot.title ("data analyze") pyplot.xlabel ("element a") pyplot.ylabel ("element b") pyplot.show () these are all the contents of the article "how to use the Python Visualization matplotlib Module" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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